[1]} proposed one of the first neural models for the NER task. They represented the different features used for NER as vectors in their model. Later, they replaced these manually extracted features with word vectors [2]}. [3]} proposed an LSTM-based neural model with word embeddings for this task and achieved a high F1 score on the CoNLL 2003 dataset. [4]} and [5]} used character-based embeddings along with word embeddings for this task. Their experiments on the CoNLL 2003 dataset show that character-level embeddings help in improving the performance of the task. [6]} also used an LSTM-CRF model for this task and used CoNLL 2002 and 2003 datasets for their experiments. Recently, contextualized word representations such as ELMo [7]} and BERT [8]} have proven very effective for the NER task. Models with such representation [9]} achieved state-of-the-art performance on the CoNLL 2003 dataset.
